Stream into Apache Iceberg with your free account
Deliver data from databases, SaaS apps, APIs, files, and streaming systems into Apache Iceberg using Estuary's pre-built connectors. Run pipelines from real-time streaming to scheduled batch based on connector capabilities.
- <100ms to batch
- 200+ connectors
- Managed data pipelines
Apache Iceberg connector details
The Apache Iceberg connector materializes Estuary collections into Iceberg tables for large-scale analytics and lakehouse architectures. It orchestrates Spark jobs on AWS EMR Serverless (or another configured compute backend) to merge incoming changes, ensuring tables stay continuously up to date.
- Materializes Estuary collections into Apache Iceberg tables for open table formats
- Orchestrates Spark jobs on AWS EMR Serverless to process updates
- Supports hard deletes for accurate CDC replication or soft deletes for cost savings
- Compatible with AWS Glue, S3 Tables, and Snowflake Open Catalogs via REST APIs
- Configurable sync schedules to balance performance and cost
How to connect your data source to Apache Iceberg in 3 easy steps
- 1
Connect your data source
Select from more than 100 supported databases and SaaS platforms including PostgreSQL, MySQL, SQL Server, MongoDB, and Kafka.
- 2
Prepare and transform your data
Apply transformations and schema mapping as data moves whether you are streaming in real time or loading in batches.
- 3
Sync to Apache Iceberg
Continuously or periodically deliver data into your destination with support for change data capture and reliable delivery for accurate insights.
Learn more with some related videos
Dive deeper into Apache Iceberg with tutorials and walkthroughs from our YouTube channel.
![How to Load Data into Apache Iceberg (S3 + Glue Tutorial) video thumbnail]()
How to Load Data into Apache Iceberg (S3 + Glue Tutorial)
Learn about the Apache Iceberg table format, why it’s essential for organizing your data lake, and how to load data into Iceberg using Estuary. We’ll cover a brief intro to Iceberg before demoing the connector setup with Estuary, Amazon S3, and AWS Glue for real-time and batch data integration.
![Streaming Data Lakehouse Tutorial: MongoDB to Apache Iceberg video thumbnail]()
Streaming Data Lakehouse Tutorial: MongoDB to Apache Iceberg
Learn how to connect MongoDB to Apache Iceberg in Iceberg table format using Estuary. In this step-by-step demo, we show you how to:
![PostgreSQL to Iceberg - Streaming Lakehouse Foundations video thumbnail]()
PostgreSQL to Iceberg - Streaming Lakehouse Foundations
Stream Real-Time Data from Postgres to Iceberg with Change Data Capture and Estuary. In this step-by-step tutorial, we demonstrate how to set up and stream real-time data from a PostgreSQL database into Iceberg tables using change data capture (CDC) with Estuary. Learn how to capture, ingest, and materialize data using Estuary's seamless integration. This demo uses a sales database to showcase how changes in a PostgreSQL table are tracked and replicated into an Iceberg table stored in AWS S3.
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HIGH THROUGHPUT
Distributed, event-driven architecture scales for demanding data workloads.
DURABLE COLLECTIONS
Store data as it moves so you can transform, replay, and deliver it downstream.
FLEXIBLE LATENCY
From <100ms CDC and real-time streaming to scheduled batch, depending on connector capabilities.
From <100ms to batch
Estuary supports CDC, real-time streaming, incremental syncs, and scheduled batch across its connector ecosystem. Bring data from databases, SaaS apps, APIs, files, and event streams into Apache Iceberg, with capture and delivery cadence based on connector capabilities and pipeline configuration.
- Connect data from operational databases, SaaS applications, APIs, files, and streaming systems to Apache Iceberg through Estuary.
- Transform and route data through Estuary collections before delivering it to Apache Iceberg.
See what you can connect to Apache Iceberg:
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Pipelines as fast as Kafka, easy as managed ELT/ETL, cheaper than building it.
Feature Comparison
| Estuary | Batch ELT/ETL | DIY Python | Kafka | |
|---|---|---|---|---|
| Price | $ | $$-$$$$ | $-$$$$ | $-$$$$ |
| Latency | <100ms to scheduled | 5min+ | Varies | <100ms |
| Ease | Analysts can manage | Analysts can manage | Data Engineer | Senior Data Engineer |
| Scale | ||||
| Maintenance Effort | Low | Medium | High | High |
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